Data Breach – Its Effects on Industry

In this Digital world, Data has become one of the most crucial parts in every field. To protect this sensitive piece of information many methods and technologies are coming into existence. A data breach reveals sensitive, protected and confidential information to an unauthorized person. Increasingly opportunities exist for information to leak out as our computers and mobile devices become more associated. Data leaks pose a serious threat to companies and can cost them significantly either financially and reputationally. The long-term effects of a data breach can spread throughout a company, having an effect on all parties involved, including the user base, staff, and cybersecurity teams in charge of repair. By giving priority to the most frequently attacked industries, this article will advance understanding about data hacking incidents and aid in securing corporate data

Using Federated Artificial Intelligence System of Intrusion Detection for IoT Healthcare System Based on Blockchain

Recently Internet of things (IoT)-based healthcare system has expanded significantly, however, they are restricted by the absence of an intrusion detection mechanism (IDS). Modern technologies like blockchain (BC), edge computing (EC), and machine learning (ML) provide a robust security solution that is well-suited to protecting patients' medical information. In this study, we offer an intelligent intrusion detection mechanism FIDANN that protects the confidentiality of medical data by completing the intrusion detection task by utilising Dwarf mongoose-optimized artificial neural networks (DMO-ANN) through a federated learning (FL) technique. In the context of recent developments in blockchain technology, such as the elimination of contaminating attacks and the provision of complete visibility and data integrity over the decentralized system with minimal additional effort. Using the model at the edges secures the cloud from attacks by limiting information from its gateway with less computing time and processing power as FL works with fewer datasets. The findings demonstrate that our suggested models perform better when dealing with the diversity of data produced by IoT devices